Electricity peak demand classification with artificial neural networks

Gajowniczek, Krzysztof; Nafkha, Rafik; Ząbkowski, Tomasz

doi:10.15439/2017f168

Cited by 24 publications

(18 citation statements)

References 20 publications

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“…For example, the effects of alteration and high load variability during Easter, All Saints' Day and Christmas, winter and summer holidays are clearly detectable (see annotations in Figure 1c). These are typical peak load periods for European countries [4,34].…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, moreover, there has been a steady trend towards the electrification of energy consumption, linked to the need for greater use of renewable energy sources, which results in a load profile increasingly characterized by the presence of peaks in consumption due to human behavior, that can lead to problems for electricity providers [4]. The presence of peaks in consumption and generation linked to human behaviour and renewable sources leads to the research of mitigation techniques, for example using a variable pricing system that pushes users to plan as much as possible the use of energy resources, in addition to the use of distributed storage systems, as tested in the project H2020 NETfficient, in which the techniques described in this work have found application [5].…”

Section: Introductionmentioning

confidence: 99%

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Smart Meter Forecasting from One Minute to One Year Horizons

Massidda

Marrocu

2018

Energies

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The ability to predict consumption is an essential tool for the management of a power distribution network. The availability of an advanced metering infrastructure through smart meters makes it possible to produce consumption forecasts down to the level of the individual user and to introduce intelligence and control at every level of the grid. While aggregate load forecasting is a mature technology, single user forecasting is a more difficult problem to address due to the multiple factors affecting consumption, which are not always easily predictable. This work presents a hybrid machine learning methodology based on random forest (RF) and linear regression (LR) for the deterministic and probabilistic forecast of household consumption at different time horizons and resolutions. The approach is based on the separation of long term effects (RF) from short term ones (LR), producing deterministic and probabilistic forecasts. The proposed procedure is applied to a public dataset, achieving a deterministic forecast accuracy much higher than other methodologies, in all scenarios analyzed. This covers horizons of forecast from one minute to one year, and highlights the great added value provided by probabilistic forecasting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smart Meter Forecasting from One Minute to One Year Horizons

Massidda

Marrocu

2018

Energies

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“…Common programming languages for this kind of simulation tools are Matlab, R [86], GradeStat [87], Python [88], or Java [89]. Examples of the use of statistical techniques to create forecasting models of energy consumption and load demand peak are described in references [90][91][92].…”

Section: Building Energy Demand Modellingmentioning

confidence: 99%

Simulation Tools to Build Urban-Scale Energy Models: A Review

et al. 2018

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The development of Urban-Scale Energy Modelling (USEM) at the district or city level is currently the goal of many research groups due to the increased interest in evaluating the impact of energy efficiency measures in city environments. Because USEM comprises a great variety of analysis areas, the simulation programs that are able to model urban-scale energy systems actually consist of an assemblage of different particular sub-models. In order to simulate each of the sub-models in USEM, one can choose to use either existing specific simulation engines or tailor-made models. Engines or tools for simulation of urban-scale energy systems have already been overviewed in previous existing literature, however the distinction and classification of tools according to their functionalities within each analysis area in USEM has not been clearly presented. Therefore, the present work aims at reviewing the existing tools while classifying them according to their capabilities. The ultimate goal of this classification is to expose the available resources for implementing new co-simulation approaches in USEM, which may reduce the modelling effort and increase reliability as a result of using established and validated simulation engines.

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“…However, there are other factors that are likely to influence users' electrical consumption; therefore, it is not trivial to detect high loads in advance. Luckily, peak loads usually follow similar patterns [49,50], and these can be identified with, inter alia, classification and pattern recognition algorithms to be further used to improve the accuracy of the forecasts, as presented in our paper. For instance, in [6], the authors proposed the approach to predict electricity consumption peaks as an input to load balancing and price incentive strategies.…”

Section: Literature Review On Related Problemsmentioning

confidence: 99%

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

Gajowniczek

Ząbkowski

2017

Energies

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Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution) and deliver accurate forecasts, with mean absolute percentage error (MAPE) of 3.10% and resistant mean absolute percentage error (r-MAPE) of 2.70% for the 24 h forecasting horizon.

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Electricity peak demand classification with artificial neural networks

Cited by 24 publications

References 20 publications

Smart Meter Forecasting from One Minute to One Year Horizons

Smart Meter Forecasting from One Minute to One Year Horizons

Simulation Tools to Build Urban-Scale Energy Models: A Review

Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

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